Trend and Forecast Analysis of Indonesia’s Inflation Using ARIMA and Neural Network Model
This study aims to compare forecast performance of Neural Network (NN) to ARIMA in the case of Indonesia’s inflation and to find if there is any interesting trend in Indonesia’s inflation. We use year-on-year monthly Indonesia’s inflation data from 2006:12 to 2018:12 released by Bank Indonesia (BI) and the Indonesian Central Bureau of Statistics (CBS). We divide the series into 3 data series to capture the trend in the inflation (i.e DS1, DS2 and DS3). The data set 1 (DS1) covers data from 2006:12 to 2014:08, DS2 from 2006:12 to 2018:12, dan DS3 from 2010:12 to 2018:12. The series is then processed using the standard ARIMA method and NN model. We found that the NN model outperforms the ARIMA model in forecasting inflation for each respective series by analysing its Root Mean Squared Error (RMSE). We also found that short term lagged-inflation (backward-looking) variable has lesser effect on inflation compared to the more recent series.